class_docstr: A validator for PySpark UDF signatures.
docstr: Get the parameters that correspond to the inputs for a UDF.
  
  Args:
      udf (Callable[..., Any]): the user provided function.
docstr_tok: ["Get","the","parameters","that","correspond","to","the","inputs","for","a","UDF",".","Args",":","udf","(","Callable","[","...",",","Any","]",")",":","the","user","provided","function","."]
code: def _get_input_params(self, udf: Callable[..., Any]) -> List[str]:
      """Get the parameters that correspond to the inputs for a UDF.
  
      Args:
          udf (Callable[..., Any]): the user provided function.
      """
      parameters = list(inspect.signature(udf).parameters.keys())
  
      # Remove non-input parameter names.
      if "params" in parameters:
          parameters.remove("params")
      if "spark" in parameters:
          parameters.remove("spark")
  
      return parameters
  
imports: from __future__ import absolute_import
  import inspect
  import re
  from abc import ABC, abstractmethod
  from typing import Any, Callable, List
  import attr
  from sagemaker.feature_store.feature_processor._data_source import FeatureGroupDataSource, BaseDataSource
  from sagemaker.feature_store.feature_processor._feature_processor_config import FeatureProcessorConfig
  from sagemaker.feature_store.feature_processor._input_offset_parser import InputOffsetParser
label_desc: Use image node_id 2 for calling the SparkUDFSignatureValidator obj's underlying member method code with example usage: obj._get_input_params(udf) and returns: parameters